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E-raamat: Neural Network Modeling and Identification of Dynamical Systems

(Full Professor, Computer-Aided Design Department, Department of Flight Dynamics and Control, Numerical Mathematics and Computer Programming Department, Moscow Aviation Institute, Russia), (Affiliation: Senior R&D Software Engineer, Robo)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 17-May-2019
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128154304
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Neural Network Modeling and Identification of Dynamical SystemsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 17-May-2019
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128154304
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Neural Network Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive neural network models for complex systems that are typically found in real-world applications. The book introduces the theoretical knowledge available for the modeled system into the purely empirical black box model, thereby converting the model to the gray box category. This approach significantly reduces the dimension of the resulting model and the required size of the training set. This book offers solutions for identifying controlled dynamical systems, as well as identifying characteristics of such systems, in particular, the aerodynamic characteristics of aircraft.

  • Covers both types of dynamic neural networks (black box and gray box) including their structure, synthesis and training
  • Offers application examples of dynamic neural network technologies, primarily related to aircraft
  • Provides an overview of recent achievements and future needs in this area
About the Authors vii
Preface ix
Acknowledgment xiii
List of Acronyms
xv
List of Notations
xvii
Introduction 1(6)
1 The Modeling Problem for Controlled Motion of Nonlinear Dynamical Systems
1.1 The Dynamical System as an Object of Study
7(14)
1.1.1 The General Concept of a System
7(4)
1.1.2 Classes of Dynamical Systems
11(3)
1.1.3 Classes of Environments
14(1)
1.1.4 Interaction Between Systems and Environment
15(1)
1.1.5 Formalization of the Dynamical System Concept
16(4)
1.1.6 Behavior and Activity of Systems
20(1)
1.2 Dynamical Systems and the Problem of Adaptability
21(7)
1.2.1 Types of Adaptation
22(1)
1.2.2 General Characteristics of the Adaptive Control Problem
23(1)
1.2.3 Basic Structural Variants of Adaptive Systems
24(3)
1.2.4 The Role of Models in the Problem of Adaptive Control
27(1)
1.3 A General Approach to Dynamical System Modeling
28(7)
1.3.1 A Scheme of the Modeling Process for Dynamical Systems
28(4)
1.3.2 The Main Problems That Need to Be Solved During Design of a Model for a Dynamical System
32(1)
References
33(2)
2 Dynamic Neural Networks: Structures and Training Methods
2.1 Artificial Neural Network Structures
35(16)
2.1.1 Generative Approach to Artificial Neural Network Design
35(4)
2.1.2 Layered Structure of Neural Network Models
39(8)
2.1.3 Neurons as Elements From Which the ANN Is Formed
47(2)
2.1.4 Structural Organization of a Neuron
49(2)
2.2 Artificial Neural Network Training Methods
51(16)
2.2.1 Overview of the Neural Network Training Framework
52(6)
2.2.2 Static Neural Network Training
58(4)
2.2.3 Dynamic Neural Network Training
62(5)
2.3 Dynamic Neural Network Adaptation Methods
67(6)
2.3.1 Extended Kalman Filter
67(2)
2.3.2 ANN Models With Interneurons
69(3)
2.3.3 Incremental Formation of ANN Models
72(1)
2.4 Training Set Acquisition Problem for Dynamic Neural Networks
73(20)
2.4.1 Specifics of the Process of Forming Data Sets Required for Training Dynamic Neural Networks
73(1)
2.4.2 Direct Approach to the Process of Forming Data Sets Required for Training Dynamic Neural Networks
73(7)
2.4.3 Indirect Approach to the Acquisition of Training Data Sets for Dynamic Neural Networks
80(8)
References
88(5)
3 Neural Network Black Box Approach to the Modeling and Control of Dynamical Systems
3.1 Typical Problems Associated With Development and Maintenance of Dynamical Systems
93(1)
3.2 Neural Network Black Box Approach to Solving Problems Associated With Dynamical Systems
94(5)
3.2.1 Main Types of Models
94(1)
3.2.2 Approaches to Consideration of Disturbances Acting on a Dynamical System
95(4)
3.3 ANN-Based Modeling and Identification of Dynamical Systems
99(3)
3.3.1 Feedforward Neural Networks for Modeling of Dynamical Systems
99(2)
3.3.2 Recurrent Neural Networks for Modeling of Dynamical Systems
101(1)
3.4 ANN-Based Control of Dynamical Systems
102(29)
3.4.1 Adjustment of Dynamic Properties of a Controlled Object Using Artificial Neural Networks
102(10)
3.4.2 Synthesis of an Optimal Ensemble of Neural Controllers for a Multimode Aircraft
112(14)
References
126(5)
4 Neural Network Black Box Modeling of Nonlinear Dynamical Systems: Aircraft Controlled Motion
4.1 ANN Model of Aircraft Motion Based on a Multilayer Neural Network
131(3)
4.1.1 The General Structure of the ANN Model of Aircraft Motion Based on a Multilayer Neural Network
131(2)
4.1.2 Learning of the Neural Network Model of Aircraft Motion in Batch Mode
133(1)
4.1.3 Learning of the Neural Network Model of Aircraft Motion in Real-Time Mode
133(1)
4.2 Performance Evaluation for ANN Models of Aircraft Motion Based on Multilayer Neural Networks
134(5)
4.3 Application of ANN Models to Adaptive Control Problems for Nonlinear Dynamical Systems Operating Under Uncertainty Conditions
139(26)
4.3.1 The Demand for Adaptive Systems
139(1)
4.3.2 Model Reference Adaptive Control
140(14)
4.3.3 Model Predictive Control
154(4)
4.3.4 Adaptive Control of Angular Aircraft Motion Under Uncertainty Conditions
158(4)
References
162(3)
5 Semiempirical Neural Network Models of Controlled Dynamical Systems
5.1 Semiempirical ANN-Based Approach to Modeling of Dynamical Systems
165(5)
5.2 Semiempirical ANN-Based Model Design Process
170(7)
5.3 Semiempirical ANN-Based Model Derivatives Computation
177(10)
5.4 Homotopy Continuation Training Method for Semiempirical ANN-Based Models
187(5)
5.5 Optimal Design of Experiments for Semiempirical ANN-Based Models
192(7)
References
196(3)
6 Neural Network Semiempirical Modeling of Aircraft Motion
6.1 The Problem of Motion Modeling and Identification of Aircraft Aerodynamic Characteristics
199(1)
6.2 Semiempirical Modeling of Longitudinal Short-Period Motion for a Maneuverable Aircraft
200(8)
6.3 Semiempirical Modeling of Aircraft Three-Axis Rotational Motion
208(8)
6.4 Semiempirical Modeling of Longitudinal Translational and Angular Motion for a Maneuverable Aircraft
216(95)
References
225(86)
A. Results of Computational Experiments With Adaptive Systems
Index 311
Dr. Yury V. Tiumentsev is currently a full professor at Moscow Aviation Institute, teaching in subjects including computer science, computer-aided design, artificial intelligence, artificial neural networks, and soft computing. He is also the Vice President of the Russian Neural Network Society and Vice-Chairman of the Organization and Program Committee of the Annual All-Russia Scientific and Engineering Conference on Neuroinformatics. Dr. Tiumentsev is also a member of the Scientific Committee and a publication reviewer for the International Conference of Artificial Intelligence and Soft Computing (ICAISC), as well as other conference collections such as the International Joint Conference on Neural Networks (IJCNN). His current research subjects include artificial neural networks, adaptive systems, intelligent control, mathematical modeling and computer simulation of complex systems. Dr. Tiumentsev is the author of the Russian-language monograph entitled Neural Network Modeling of Aircraft Motion, and has also written more than 130 articles on his areas of expertise. Mikahil Egorchev is currently a Senior R&D Software Engineer at RoboCV. He is presently working on his Ph.D. in Mathematical Modeling, Numerical Methods and Software Complexes at the Moscow Aviation Institute. He has published 13 articles in his subject areas, which include artificial neural networks, mathematical modeling and computer simulation of nonlinear dynamical systems, numerical optimization methods, and optimal control.
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